Distributed sensor calibration

ABSTRACT

Methods, systems, and devices are described for calibrating a sensor of a distributed sensor system. More specifically, the described features generally relate to calibrating one sensor of such a system using information from one or more other sensors of the system. A calibration model may be determined based at least in part on a difference in geospatial location of the sensors. Further, in the case of one or both sensors being mobile, the difference in geospatial location of the sensors may vary over time such that different calibration models may apply to different portions of the sensed data. Also, the relative quality of the data sensed by the sensors may be taken into account for calibration (e.g., directionality of the calibration).

BACKGROUND

Field of the Disclosure

The present disclosure, for example, relates to calibrating sensors of adistributed sensor system, and more particularly to calibrating onesensor using information from one or more other sensors.

Description of Related Art

Distributed sensor systems may be deployed to collect data over an areaor areas of interest. For example, environmental data (e.g., dataincluding measurements of environmental conditions) may be collectedusing a plurality of sensor assemblies in different physical locationsof such area(s) of interest. Such systems may employ relatively low costsensors, which generally may provide lower quality data thanscientific-grade instruments, for instance. Further, the sensors of suchsystems may be subject to outdoor conditions, and may suffer from decayand/or drifting, for example.

Calibration of environmental sensors may be important to ensure thatdata collected is useful for a particular purpose (e.g., accurate,reliable, etc.). This is particularly true when aggregating data from aplurality of sensors as in a distributed sensor system. However,calibration may be difficult and complex, particularly when the systemincludes sensors of different types and/or from different manufacturers.Employing on-site calibration, known automated calibration systems, or areference standard for each sensor of the system may be costprohibitive.

SUMMARY

The described features generally relate to one or more improved systems,methods, and/or apparatuses for calibrating sensors of a distributedsensor system. More specifically, the described features generallyrelate to calibrating one sensor of such a system using information fromone or more other sensors of the system. A calibration model may bedetermined based at least in part on a difference in geospatial locationof the sensors. Further, in the case of one or both sensors beingmobile, the difference in geospatial location of the sensors may varyover time such that different calibration models may apply to differentportions of the sensed data. Also, the relative quality of the datasensed by the sensors, which may be related to the sensors themselves orother conditions affecting the data, may be taken into account forcalibration (e.g., directionality of the calibration).

A method for calibrating an environmental sensor is described. Themethod may include: collecting sensed data from a first environmentalsensor and a second environmental sensor of the distributedenvironmental sensor system; determining a difference in geospatiallocation between a location of the first environmental sensor and alocation of the second environmental sensor; determining a calibrationmodel based at least in part on the determined difference in geospatiallocation; and, calibrating the first environmental sensor using thedetermined calibration model and the sensed data of the secondenvironmental sensor.

In some aspects, calibrating the first environmental sensor may involvecorrelating the sensed data of the first environmental sensor with thesensed data of the second environmental sensor.

In some aspects, calibrating the first environmental sensor may involveperforming a frequency-based decomposition or frequency-based filteringof the sensed data of the first and second environmental sensors. Insuch examples, determining the calibration model may involve selecting aportion of the decomposed or filtered data of the first environmentalsensor and a portion of the decomposed data of the second environmentalsensor. Such selection may be based at least in part on the determineddifference in geospatial location. Further, calibrating the firstenvironmental sensor may involve correlating or setting equal theselected portions of the decomposed or filtered data of the first andsecond environmental sensors.

In some aspects, the method may include determining a difference in aquality between the sensed data of the first environmental sensor and aquality of the sensed data of the second environmental sensor. Adirectionality of the calibration may be determined based at least inpart on the determined difference in the quality. For example, thecalibration of the first environmental sensor may occur when the qualityof the sensed data of the first environmental sensor is lower than thequality of the sensed data of the second environmental sensor. Thequality of the sensed data of at least one of the first environmentalsensor and the second environmental sensor may be based at least in parton an amount of the sensed data provided by that environmental sensor, adata history of that environmental sensor, a periodicity of dataprovided by that environmental sensor, a calibration history of thatenvironmental sensor, or a predetermined characteristic of thatenvironmental sensor.

In some aspects, calibrating the first environmental sensor may involveselecting a portion of the sensed data of the first environmental sensorand a corresponding portion of the sensed data of the secondenvironmental sensor based at least in part on the determined differencein geospatial location over time.

In some aspects, the method may include determining a secondaryparameter that affects the sensed data of the first environmentalsensor. In such case, the sensed data of the first environmental sensormay be adjusted based on an effect of the parameter, with the adjustmentbeing performed prior to performing the calibration. In variousexamples, a secondary parameter may be determined at a calibrationdevice, provided to the calibration device by one or more sensors, orotherwise provided to the calibration device.

In some aspects, the method may involve calibrating the secondenvironmental sensor using the determined calibration model and thesensed data of the first environmental sensor.

A calibration device for calibrating an environmental sensor of adistributed environmental sensor system is described. The apparatus mayinclude a processor and memory communicatively coupled with theprocessor. The memory may include computer-readable code that, whenexecuted by the processor, causes the device to: collect sensed data ofa first environmental sensor and a second environmental sensor of thedistributed environmental sensor system; determine a difference ingeospatial location between a location of the first environmental sensorand a location of the second environmental sensor; determine acalibration model based at least in part on the determined difference ingeospatial location; and, calibrate the first environmental sensor usingthe determined calibration model and the sensed data of the secondenvironmental sensor. The memory may include computer-readable codethat, when executed by the processor, causes the device to performfeatures of the method described above and further herein.

A non-transitory computer-readable medium is described. The medium mayinclude computer-readable code that, when executed, causes a device to:collect sensed data of a first environmental sensor and a secondenvironmental sensor of the distributed environmental sensor system;determine a difference in geospatial location between a location of thefirst environmental sensor and a location of the second environmentalsensor; determine a calibration model based at least in part on thedetermined difference in geospatial location; and, calibrate the firstenvironmental sensor using the determined calibration model and thesensed data of the second environmental sensor. The medium may includecomputer-readable code that, when executed, causes a device to performfeatures of the method described above and further herein.

The foregoing has outlined rather broadly the features and technicaladvantages of examples according to the disclosure in order that thedetailed description that follows may be better understood. Additionalfeatures and advantages will be described hereinafter. The conceptionand specific examples disclosed may be readily utilized as a basis formodifying or designing other structures for carrying out the samepurposes of the present disclosure. Such equivalent constructions do notdepart from the scope of the appended claims. Characteristics of theconcepts disclosed herein, both their organization and method ofoperation, together with associated advantages will be better understoodfrom the following description when considered in connection with theaccompanying figures. Each of the figures is provided for the purpose ofillustration and description only, and not as a definition of the limitsof the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of the presentinvention may be realized by reference to the following drawings. In theappended figures, similar components or features may have the samereference label. Further, various components of the same type may bedistinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

FIG. 1 shows a block diagram of a distributed sensor system and acalibration device associated therewith, in accordance with variousaspects of the present disclosure;

FIG. 2 shows a diagram illustrating a time varying difference ingeospatial location between sensor assemblies, in accordance withvarious aspects of the present disclosure;

FIG. 3A shows a diagram illustrating frequency-based filtering of senseddata, in accordance with various aspects of the present disclosure;

FIG. 3B shows a diagram illustrating frequency-based filtering of senseddata and calibration models for the sensors depicted in FIG. 3A, inaccordance with various aspects of the present disclosure;

FIG. 4A shows a block diagram of an example of a device configured foruse in calibrating a sensor of a distributed sensor system, inaccordance with various aspects of the present disclosure;

FIG. 4B shows a block diagram of an example of a device configured foruse in calibrating a sensor of a distributed sensor system, inaccordance with various aspects of the present disclosure; and

FIG. 5 is a flow chart illustrating an example of a method forcalibrating a sensor of a distributed sensor system, in accordance withvarious aspects of the present disclosure.

DETAILED DESCRIPTION

This description discloses techniques for calibrating a sensor of adistributed sensor system. The described calibrating techniques use acalibration model and sensed data of another sensor of the system. Thecalibration model may be determined based at least in part on adifference in geospatial location between a location of the sensor to becalibrated and a location of the sensor to be used for calibration.Further, a portion of the sensed data of the sensor to be calibrated anda corresponding portion of the sensed data of the other sensor may beselected for performing the calibration. Such selection may be based atleast in part on the determined difference in geospatial location.

Various calibration models may be used to correlate the sensed data ofthe sensor being calibrated with the sensed data of the other sensor.For example, calibrating the sensor may involve a frequency-baseddecomposition or frequency-based filtering of the sensed data of bothsensors. Based at least in part on the determined difference ingeospatial location, a calibration model may be selected that employs arelatively low frequency decomposition or filtering of the sensed datafor correlation, a relatively high frequency decomposition or filteringof the sensed data for correlation, or both. Various calibration modelsmay be used to adjust how sensed data of an environmental condition isreported from a physical measurement at a sensor. For instance, acalibration model may perform an adjustment to a sensor gain, a sensorgain exponent, a sensor offset, or any other parameter or combination ofparameters used by a sensor or sensor system to convert a measurement toa reported condition. Such adjustments may be made directly at a sensor,at a portion of a sensor assembly that contains the sensor, or at anyother device that receives sensed data from the sensor. Through variousexamples, these calibration models can be applied to improve correlationbetween sensors in a distributed sensor system and provide higherquality data from the system.

The following description provides examples, and is not limiting of thescope, applicability, or examples set forth in the claims. Changes maybe made in the function and arrangement of elements discussed withoutdeparting from the scope of the disclosure. Various examples may omit,substitute, or add various procedures or components as appropriate. Forinstance, the methods described may be performed in an order differentfrom that described, and various steps may be added, omitted, orcombined. Also, features described with respect to some examples may becombined in other examples.

Referring first to FIG. 1, a block diagram illustrates an example of adistributed sensor system 100 in accordance with various aspects of thepresent disclosure. The distributed sensor system 100 may include aplurality of sensor assemblies 110 distributed over an area of interest105, and a central processing device 115. The sensor assemblies 110 maybe, for example, configured to monitor and/or assess environmentalconditions that affect air quality, such as carbon monoxide, carbondioxide, nitrogen oxides, sulfur dioxide, particulates, etc. To supportsuch measurements, each of the sensor assemblies 110 may include one ormore sensors 111, such as sensor assembly 110-a having a single sensorand sensor assembly 110-d having a plurality of sensors. The sensors 111may measure different environmental conditions and/or may measure someof the same environmental conditions using the same or disparatetechnologies. Also, the sensors 111 or the sensor assemblies 110 may beof different quality and/or may provide sensed data that is of differentquality, or is characterized by different qualities.

Each of the sensor assemblies 110 may be associated with one or moreenvironmental conditions that are communicated in the distributed sensorsystem 100. For instance, a sensor assembly 110 may transmit sensed databy way of a wired or wireless communication system. Thus, each sensorassembly 110 may include hardware and/or software to providecommunication through the distributed sensor system 100. For example,each sensor assembly 110 may communicate with the central processingdevice 115 via wireless links 120 using any suitable wirelesscommunication technology, or via a wired link (not shown). Althoughwireless links 120 are not shown between each of the sensor assemblies110 and the central processing device 115 for the sake of simplicity, itshould be understood that the central processing device 115 maycommunicate with all of the sensor assemblies 110 of the system 100,which may include wired or wireless communication links from one of thesensor assemblies 110, through another of the sensor assemblies 110, andeventually to the central processing device 115.

The central processing device 115 can receive sensed data from thesensor assemblies 110, and provide various functions. For instance, thecentral processing device 115 may aggregate data and perform variouscalculations with the received data. In some examples the centralprocessing device 115 can perform reporting or alerting functions basedon the received data. In some examples the central processing device 115may include a calibration model for one or more sensors 111 that providesensed data to the central processing device. In this way, calibrationmodels may be stored at a central location, which may be in addition to,or instead of a calibration model stored locally at a sensor 111 or asensor assembly 110. In some examples the central processing device 115can transmit any of the received data, calculations made from thereceived data, reports, or alerts to one or more external devices by wayof an external communication link 125.

Each of the sensor assemblies 110 may also measure secondary parametersthat may or may not be related to the environmental conditions that arecommunicated through the network. In some examples, secondary parametersmay support the measurement and/or calculation of environmentalconditions that are communicated through the network. For example, themeasurement of an atmospheric concentration of a particular gas mayrequire secondary measurements of temperature and/or atmosphericpressure, in addition to a sensor 111. In some examples, the calibrationof a sensor 111 associated with an environmental condition may be afunction of a secondary parameter. For instance, a sensor gain or sensoroffset for a sensor 111 that measures a concentration of an atmosphericconstituent may be based on temperature. In such examples, a sensorassembly 110 that reports the concentration of the atmosphericconstituent to the network may also measure temperature in order tovalidate, or otherwise determine a quality of the reported data.

In other examples, a sensor assembly 110 may measure secondaryparameters that are not directly related to the measurement orcalculation of an environmental condition, but are otherwise related tosupporting the functionality of the sensor assembly 110. For instance,secondary parameters measured by the sensor assembly 110 may furtherinclude a sensor assembly power level, a sensor assembly battery chargelevel, a communication throughput, a network status parameter, clocktime, sensor run time, or any other secondary parameter related tosupporting the operation of the sensor assembly 110.

A calibration device 130 is also illustrated in FIG. 1. As shown, thecalibration device 130 may communicate with the sensor assemblies 110via wireless communication links 135 using any suitable wirelesscommunication technology, or via a wired link (not shown). Althoughwireless communication links 135 are not shown between each of thesensor assemblies 110 and the calibration device 130 for the sake ofsimplicity, it should be understood that the calibration device 130 maycommunicate with all of the sensor assemblies 110 of the system 100,which may include wired or wireless communication links from one of thesensor assemblies 110, through another of the sensor assemblies 110, andeventually to the calibration device 130. Furthermore, although thecalibration device 130 and the central processing device 115 are shownas separate devices, in various examples the two devices, or any of thedescribed functionality of the two devices, may be embodied in the samedevice.

The calibration device 130 may receive sensed data from sensors 111 astransmitted by the sensor assemblies 110, as well as an identity of eachsensor 111 and/or sensor assembly 110 that is associated with therespective sensed data. In some examples the calibration device 130 maydetermine a location of a sensor 111 by looking up a predeterminedlocation stored in memory associated with the calibration device 130. Insome examples the calibration device 130 may receive informationassociated with the location of a sensor 111, which may be received froma central database, such as a database stored at the central processingdevice 115, or received from the sensor assembly 110 that includes thesensor 111. In examples where the sensor assembly 110 transmits locationinformation for a sensor 111, the location information may bepredetermined information stored at the sensor assembly 110, or may beotherwise measured by the sensor assembly 110. For instance, in someexamples the sensor assembly 110 may be configured to determine its ownlocation by receiving signals from a GPS or GLONASS satelliteconstellation, or any terrestrial or satellite-based system thatprovides positioning signals. In some examples, the location of thesensor 111 as determined by the sensor assembly 110 can be transmittedto the calibration device 130.

As discussed in further detail below, the calibration device 130 maydetermine a calibration model for one or more of the sensors 111. Insome examples the calibration device 130 may transmit an instruction toadjust a parameter associated with the calibration of a sensor 111. Forinstance, the calibration device 130 may communicate calibrationinformation (e.g., adjustments, offsets, corrections, etc.) to therespective sensor assemblies 110 to improve the correlation of thesensed data of the sensors 111, where the calibration adjustments maytake place at one or both of the sensor itself, or at another portion ofthe sensor assembly 110 that is associated with converting an output ora sensor 111 to data representing an environmental condition. In someexamples, calibration information for a plurality of sensors 111 may becommunicated to the central processing device 115, and the centralprocessing device 115 can use calibration information to adjust senseddata from the plurality to improve correlation between sensors 111 thatmeasure the same environmental condition.

The calibration device 130 may be configured to determine a differencein geospatial location between sensors 111 and then determine acalibration model to use for calibration of the sensors 111. Forexample, the calibration device 130 may determine that one of the sensorassemblies 110-a is at a relatively far distance (e.g., 1 kilometer) orproximity to another of the sensor assemblies 110-b. The relatively fardistance/proximity may allow a calibration model to be used thatcorrelates portions the sensed data from a sensor 111 of the sensorassembly 110-a reflecting a relatively steady state, or havingrelatively low frequency changes with corresponding sensed data from asensor 111 of the sensor assembly 110-b. Alternatively, the calibrationdevice 130 may determine that a different sensor assembly 110-c is at arelatively close distance (e.g., 1 meter) or proximity to the sensorassemblies 110-a. The relatively close distance/proximity may allow acalibration model to be used that correlates the sensed data from asensor 111 of the sensor assembly 110-a including both steady stateconditions and some amount of transient behavior, or otherwiserelatively higher frequency changes with corresponding sensed data froma sensor 111 of the sensor assembly 110-c.

In some examples, the sensors 111 may be cross-calibrated (e.g.,calibrating a sensor 111 of the sensor assembly 110-a using the senseddata from a sensor 111 of the sensor assembly 110-b, and calibrating thesensor assembly 110-b using the sensed data of the sensor assembly110-a). However, a directionality of the calibration may be determinedby the calibration device 130, for example, based at least in part on adifference in a quality of the sensed data of the sensors 111, where thequality of the sensed data may have a clear direction indicating arelationship from higher to lower quality, or the quality of the senseddata may reflect a particular characteristic of the sensed data.

In some examples, a first sensor 111 may be known by a calibrationdevice 130 to be a scientific-grade instrument that has well-knownand/or precisely calibrated sensor characteristics such as gain oroffset. A second sensor 111 may be known by the calibration device 130to be a lower-grade instrument characterized by greater uncertainty withrespect to the sensor characteristics such as gain or offset. In otherexamples the grades of the first sensor 111 and the second sensor 111may be otherwise provided to the calibration device 130, such as atransmission from a sensor assembly 110 having the sensor 111. In suchexamples the calibration device 130 may determine that the sensed datafrom the first sensor 111 has a higher quality than sensed data from thesecond sensor 111, based on the grades of the sensors 111 and/or thesensor assemblies 110 that include each sensor 111.

In some examples, a difference in quality may be related to an age of asensor 111 or an age of a sensor assembly 110 that includes a sensor111. For example, a first sensor 111 may be older than a second sensor111, and the first sensor 111 may have a known or unknown level ofdrift, reduced sensitivity, and/or fouling. In such examples acalibration device 130 may determine that sensed data from the secondsensor 111 has a higher quality than sensed data from the first sensor111, based on the known ages of the sensors 111.

In some examples, a difference in quality may be related to acharacteristic of the sensed data from the first and second sensors 111.For example, the first and second sensors 111 may be identicallyconstructed, and or be part of identically constructed sensor assemblies110, but sensed data from the first sensor 111 may have greater signalnoise than sensed data from the second sensor 111. In such examples acalibration device 130 may determine that sensed data from the secondsensor 111 has a higher quality than sensed data from the first sensor111, based on the known level of noise in the sensed data from each ofthe sensors 111.

In some examples, a difference in quality may be related to an amount ofdata. For example, a first sensor 111 and a second sensor 111 may beidentically constructed, but the first sensor 111 may have been deployedearlier than the second sensor 111, and sensed data from the firstsensor 111 may have a greater validation history than sensed data fromthe second sensor 111. In such examples a calibration device 130 maydetermine that sensed data from the first sensor 111 has a higherquality than sensed data from the second sensor 111, based on the knownamount of sensed data from each of the sensors 111.

In some examples, a difference in quality may be related to aperiodicity of data. For example, first and second sensors 111 may beconfigured to measure data at different rates, or during different timeperiods. In examples where sampling rates are different, it may besuitable to use data from a sensor 111 having a higher sampling rate tocontribute to the calibration of a sensor 111 having a lower samplingrate, but not vice-versa. In some examples where a first sensor 111measures an environmental condition continuously and a second sensor 111measures the environmental condition intermittently, or experiences datadrop-outs, it may be suitable to use data from the first sensor 111 tocontribute to the calibration of the second sensor 111, but not viceversa. Thus, in either case, the calibration device 130 can ascribe aquality to each of the sensors 111, and determine a difference in thequality of sensed data based on the periodicity of the sensed data

The directionality of the calibration may be from a sensor 111associated with higher quality sensed data to a sensor 111 associatedwith lower quality sensed data, for example. However, even though onesensor 111 may have higher quality components or construction thananother sensor 111, the sensor having higher quality components orconstruction may be calibrated using the sensed data of a sensor havinglower quality components or construction when the lower quality sensorprovides sensed data having higher quality, or a particular quality. Inother examples, the directionality of calibration may include adirection from a sensor 111 associated with lower quality sensed data toa sensor 111 associated with higher quality sensed data, but thecalibration effect or magnitude may be reduced as compared to ahigher-to-lower quality directionality.

FIG. 2 shows a diagram 200 illustrating a time varying difference ingeospatial location between sensors 211, in accordance with variousaspects of the present disclosure. The diagram 200 depicts sensors 211(denoted A, B and C) of a distributed sensor system, distributed withinan area of interest 205. In this example, the sensors B and C arefixed-position sensors 211, while the sensor 211-a is a mobile sensor211 that traverses a path 215 over time. The diagram 200 further depictsa timeline for the travel of the sensor 211-a showing relative distancesfrom the sensors 211-b and 211-c.

As shown, sensor 211-a may begin at an initial location that isapproximately 1 kilometer (km) from each of sensors 211-b and 211-c attime t_(i). In this example, the 1 km difference in location between thelocation of sensor 211-a and the locations of sensors 211-b and 211-cmay require sensor 211-a to be calibrated using a relatively lowfrequency decomposition or filtering of the sensed data of sensors 211-bor 211-c (or both), or vice versa. The low frequency decomposition mayinclude, for instance, sensed data that has been passed through alow-pass filter (which may vary in type depending on the nature of thedata) with a relatively low frequency. Thus, the sensed data used forcalibration around time t_(i) may be limited to data that represents arelatively steady-state condition, where transients or other relativelyhigh-frequency phenomena are removed or reduced. Alternatively, forinstance, a fast Fourier transform (FFT) or similar frequencydecomposition may be employed to remove the time domain aspect of thedata. After passing through the FFT, the magnitude of the signalconstituents may be correlated. In other examples, the distance betweentwo sensors may dictate that a decomposition simply excludes sensed datafrom being used in a calibration process when the distance is above apredetermined threshold.

As the sensor 211-a traverses the path 215, the sensor 211-a movescloser to the sensor 211-c, but remains relatively far from the sensor211-b, such as at time t₂. In this example, the 1.2 km differencebetween the location of sensor 211-a and the location of sensor 211-bmay still require sensor 211-a to be calibrated using a relatively lowfrequency decomposition or filtering of the sensed data of sensor 211-b,or vice versa. However, the 30 meter (m) difference between the locationof sensor 211-a and the location of sensor 211-c may permit anincrementally higher frequency decomposition or filtering of the senseddata of sensor 211-c for calibrating sensor 211-a, or vice versa. Forinstance, in examples where decomposition or filtering is associatedwith a low pass filter, a cutoff frequency for decomposing or filteringdata for calibration between sensors 211-a and C may be higher than acutoff frequency for decomposing or filtering data for calibrationbetween sensors 211-a and 211-b. Thus, the relatively higher frequencydecomposition of the sensed data of sensor 211-c may be used forcalibrating sensor 211-a, or vice versa. The sensed data used forcalibration at this point may include data that is sensed around thetime t₁ as well as data that is sensed around the time t₂ undercircumstances where the calibration model has not changed (e.g., lowfrequency decomposition or filtering). In some cases, however, thesensed data used for calibration at this point may be limited to datathat is sensed around the time t₂, for example, because of differencesbetween the surroundings (e.g., topography) of sensor 211-a at times t₁and t₂.

At time t₃, sensor 211-a moves still closer to sensor 211 c, but remainsrelatively far from sensor 211-b. In this example, the 10 m differencein location between the location of sensor 211-a and the location ofsensor 211 c may be small enough to use a relatively high frequencydecomposition or filtering of the sensed data of sensor 211 c forcalibrating sensor 211-a, or vice versa. For instance, as compared totime t₂, a cutoff frequency associated with a low-pass filter can beeven higher, or the sensed data may even be used in an unfilteredcondition for use in a calibration model. The sensed data used for suchcalibration at this point may be limited to data that is sensed aroundthe time t₃ while the difference in location (A-C) remains about thesame.

Also at time t₃, the 1.25 km and 10 m differences in location betweenthe location of sensor 211-a and the locations of sensors 211-b and 211c may allow sensor 211-a to be calibrated using the relatively lowfrequency decomposition of the sensed data of sensors 211-b or 211 c (orboth), or vice versa. Such calibration at this point may use data thatis sensed around the time t₁ and/or data that is sensed around the timet₂ as well as data that is sensed around the time t₃, depending on anydifferences between the surroundings the of sensor 211-a at times t₁, t₂and t₃.

At time t₄, sensor 211-a moves closer to sensor 211-b, but moves furtheraway from sensor 211 c. In this example, the 10 m difference in locationbetween the location of sensor 211-a and the location of sensor 211-bmay be small enough to use a relatively high frequency decomposition ofthe sensed data of sensor 211-b for calibrating sensor 211-a, or viceversa. The sensed data used for such calibration at this point may belimited to data that is sensed around the time t₄ while the differencein location (A-B) remains about the same.

Also at time t₄, the 10 m and 1 km differences in location between thelocation of sensor 211-a and the locations of sensors 211-b and 211 cmay allow sensor 211-a to be calibrated using the relatively lowfrequency decomposition of the sensed data of sensors 211-b or 211 c (orboth), or vice versa. Such calibration at this point may use data thatis sensed around the time t₁, around the time t₂, and/or around the timet₃ as well as data that is sensed around the time t₄, depending on anydifferences between the surroundings the of the sensor 211-a at timest₁, t₂, t₃ and t₄.

It should be understood that the number of sensors 11, whetherfixed-position or mobile, may vary and that the sensor arrangementdepicted in FIG. 2 is only an example. Further, it should be understoodthat the path 215 as well as the differences in locations set forth (1km, 1.2 km, 1.25 km, 30 m and 10 m) are only for purpose ofillustration, and that differences in geospatial locations betweensensors 211 may vary in practice and values of such differences fordetermining which calibration model to use may be determined for aparticular implementation and/or may vary (e.g., based at least in parton topography).

FIG. 3A shows a diagram 300-a illustrating a plurality of sensors 311(denoted A, B, C and D) of a distributed sensor system, in accordancewith various aspects of the present disclosure. For the sake of exampleand clarity, the sensors 311 may be fixed-location sensors. However, itshould be understood that mobility of one or more of the sensors 311 isalso possible, such as described above with respect to FIG. 2.

The diagram 300 a provides an example of how a difference in geospatiallocation may be used to determine a calibration model for calibratingone or more of the sensors. As shown, a difference in geospatiallocation between a location of sensor 311 a and a location of sensor311-c is 1 km. A difference in geospatial location between a location ofsensor 311-b and a location of sensor 311-c is also 1 km. The 1 kmdifference may be equal to or less than a first threshold difference ingeospatial location, for which a first calibration model may be used inconjunction with sensed data of sensors 311 a and/or 311-b and 311-c.The first calibration model may be used to calibrate sensor 311-c usingthe sensed data of sensor 311 a and/or the sensed data of sensor 311-b.Further, the first calibration model may be used to calibrate sensor 311a or sensor 311-b using the sensed data of sensor 311-c. Thus,calibration at a given time “t” may involve multiple sets of senseddata, allowing for sequential calibration of a sensor or combinedone-off calibration, etc.

Further, a difference in geospatial location between a location ofsensor 311 a and a location of sensor 311-d is 10 m. A difference ingeospatial location between a location of sensor B and a location ofsensor 311-d is also 10 m. The 10 m difference may be equal to or lessthan a second threshold difference in geospatial location, for which asecond calibration model may be used in conjunction with sensed data ofsensors 311 a and/or 311-b and 311-d. The second calibration model maybe used to calibrate sensor 311-d using the sensed data of sensor 311 aand/or the sensed data of sensor 311-b. Further, the second calibrationmodel may be used to calibrate sensor 311 a or sensor 311-b using thesensed data of sensor 311-d.

FIG. 3B shows a diagram 300-b illustrating frequency-based decompositionof sensed data and calibration models for the sensors depicted in FIG.3A, in accordance with aspects of the present disclosure. Thus,references to sensor A, sensor B, sensor C and sensor D correspond tosensors 311 a, 311-b, 311-c and 311-d, respectively, as described abovewith reference to FIG. 3A.

The graphical plot entitled “Decomposed Data for Calibration A→C at 1km” illustrates a frequency-based filtering 320 of sensed data (sensorreading) of sensor 311 a that may be used for calibrating sensor 311-c.As shown, the frequency-based filtering 320 of the sensed data of sensor311 a includes relatively low frequency component (solid line depictingrelatively infrequent/gradual changes in sensed data). As discussedabove with respect to FIG. 3A, the 1 km difference in geospatiallocation between sensor 311 a and sensor 311-c may be equal to or lessthan a threshold difference in geospatial location, for which thefrequency-based filtering 320 of sensed data from sensor 311 a may beused for a calibration model applied to sensor 311-c.

Similarly, the graphical plot entitled “Decomposed Data for CalibrationB→C at 1 km” illustrates a frequency-based filtering 325 of sensed data(sensor reading) of sensor 311-b that may be used for calibrating sensor311-c. As shown, the frequency-based filtering 325 of the sensed data ofsensor 311-b includes relatively low frequency component (solid linedepicting relatively infrequent/gradual changes in sensed data). In someexamples the relatively low-frequency component of two sensors that arerelatively closely spaced may be similar, or substantially the same,such as shown by the frequency-based decompositions 320 and 325. Asdiscussed above with respect to FIG. 3A, the 1 km difference ingeospatial location between sensor 311 a and sensor 311-c may be equalto or less than a threshold difference in geospatial location, for whichthe frequency-based decomposition 325 of sensed data from sensor 311-bmay be used for a calibration model applied to sensor 311-c.

The graphical plot entitled “Decomposed Data for Calibration A→D at 10m” illustrates a frequency-based filtering 330 of sensed data (sensorreading) of sensor 311 a that may be used for calibrating sensor 311-d.As shown, the frequency-based filtering 330 of the sensed data of sensor311 a includes higher-frequency content that is not reflected in thefrequency-based filtering 320, shown in the present graph for reference.The relatively higher frequency content may be appropriate in someexamples because sensor 311-d is located more closely to sensor 311 athan sensor 311-c is located to sensor 311 a. As discussed above withrespect to FIG. 3A, the 10 m difference in geospatial location betweensensor 311 a and sensor 311-d may be equal to or less than the secondthreshold difference in geospatial location, for which thefrequency-based filtering 330 of sensed data from sensor 311 a may beused for a calibration model applied to sensor 311-d.

The graphical plot entitled “Decomposed Data for Calibration B→D at 10m” illustrates a frequency-based filtering 335 of sensed data (sensorreading) of sensor 311-b that may be used for calibrating sensor 311-d.As shown, the frequency-based filtering 335 of the sensed data of sensor311-b includes higher-frequency content that is not reflected in thefrequency-based filtering 325, shown in the present graph for reference.In some examples the relatively higher-frequency component of twosensors that are relatively closely spaced may be not necessarily besimilar, such as the differences shown by the frequency-baseddecompositions 330 and 335. The relatively higher frequency content maybe appropriate in some examples because sensor 311-d is located moreclosely to sensor 311-b than sensor 311-c is located to sensor 311-b. Asdiscussed above with respect to FIG. 3A, the 10 m difference ingeospatial location between sensor 311-b and sensor 311-d may be equalto or less than the second threshold difference in geospatial location,for which the frequency-based decomposition 335 of sensed data fromsensor 311-b may be used for a calibration model applied to sensor311-d.

Turning to FIG. 4A, block diagram 400-a is shown that illustrates acalibration device 130-a for use in calibrating one or more sensors 111of a distributed sensor system 100, in accordance with various aspectsof the present disclosure. The calibration device 130-a may have variousother configurations and may be included or be part of a personalcomputer (e.g., laptop computer, netbook computer, tablet computer,etc.), a cellular telephone, a PDA, an internet appliance, a server,etc. The calibration device 130-a can have an internal power supply (notshown), such as a small battery or a solar panel, to facilitate mobileoperation, or may be powered via a another source such as a conventionalelectrical outlet (not shown) when employed in a more stationary fashion(e.g., in an office building). The calibration device 130-a is anexample of the calibration device 130 of FIG.1 and may performcalibration operations such as described herein with respect to FIGS. 1,2, 3A, 3B and/or 3C or in accordance with aspects of the methoddescribed with respect to FIG. 5.

As shown, the calibration device 130-a may include a processor 410, amemory 420, one or more transceivers 440 and/or one or more networkports 445, antennas 450, and a communications manager 430. Each of thesecomponents may be in communication with each other, directly orindirectly, over at least one bus 405.

The memory 420 can include RAM and ROM. The memory 420 may storecomputer-readable, computer-executable software (SW) 425 containinginstructions that are configured to, when executed, cause the processor410 to perform various functions described herein for calibratingsensors of a distributed sensor system 100. Alternatively, the software425 is not directly executable by the processor 410 but is configured tocause a computer (e.g., when compiled and executed) to perform functionsdescribed herein.

The processor 410 can include an intelligent hardware device, e.g., acomputer processing unit (CPU), a microcontroller, anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), etc. The processor 410 may processes informationreceived through the transceiver(s) 440 and/or to be sent to thetransceiver(s) 440 for transmission through the antennas 450.Alternatively or additionally, the processor 410 may processesinformation received through the network port(s) 445 and/or to be sentvia the network port(s) 445 to network connected sensors. The processor410 may handle, alone or in connection with the communications manager430, various aspects for communicating with the sensors of thedistributed sensor system.

The transceiver(s) 440 and the network port(s) may be configured tocommunicate bi-directionally with the sensor assemblies 110 of thedistributed sensor system, such as described with respect to FIG. 1. Thetransceiver(s) 440 can be implemented as at least one transmitter and atleast one separate receiver. While the calibration device 130-a caninclude a single antenna, there are aspects in which the calibrationdevice 130-a includes multiple antennas 450.

The communications manager 430 manages communications with the sensorassemblies 110 of the distributed sensor system 100, for example,including sensor identities, geospatial locations, sensed data, andcalibration information (e.g., corrections, adjustments, directionality,etc.). The communications manager 430 may be a component of thecalibration device 130-a in communication with some or all of the othercomponents of the calibration device 130-a over the at least one bus405. Alternatively, functionality of the communications manager 430 canbe implemented as a component of the transceiver(s) 440 and/or thenetwork port(s) 445, as a computer program product, and/or as at leastone controller element of the processor 410.

According to the architecture of FIG. 4A, the calibration device 130-afurther includes a sensor identifier 460, a sensor locator 470, acalibration model selector 480 and a secondary parameter identifier 490,each of which can be controlled by or operate in conjunction with thecommunications manager 430. The sensor identifier 460 performs variousoperations and/or procedures for identifying the sensors 111 of thedistributed sensor system 100. For example, the sensor identifier 460may determine, in addition to identity, information regarding thesensors 111 such as sensor type (including the type of data sensed andhow such data is sensed (e.g., sensor technology employed), sensorquality and/or quality of sensed data provided, etc.).

The sensor locator 470 may determine locations of the various sensors111 of the distributed sensor system 100, or may receive locations fromthe sensor assemblies 110 that include the sensors 111 themselves orfrom another source (e.g., locations provided based on a particularlayout of fixed-location sensors), and then determine a difference ingeospatial location between sensors 111 involved in a calibrationprocedure.

The calibration model selector 480 may operate in conjunction with thesensor locator 470, employing the determined geospatial difference(s)between sensors 111 to determine an appropriate calibration model to beused for calibrating one or more of such sensors 111. The determinedcalibration model(s) may be implemented by the processor 410 inconjunction with the memory 420 (e.g., storing calibration models in theSW 425 to be executed by the processor 410).

The secondary parameter identifier 490 may be configured to determinewhether the sensed data of an identified sensor 111 is affected by asecondary parameter (e.g., temperature). For example, the secondaryparameter identifier 490 may determine a secondary parameter based atleast in part on the type of data sensed and/or the technology employedfor the sensor 111. Once identified, the processor 410 may adjust orcorrect the sensed data associated with the sensor 111 to mitigate theeffect of the secondary parameter on the sensed data, for example, byimplementing an adjustment procedure stored in the memory 420.

Thus, the components of the calibration device 130-a may be configuredto implement aspects discussed above with respect to FIGS. 1, 2, 3A, 3Band 3C. Moreover, the components of the calibration device 130-a may beconfigured to implement aspects discussed below with respect to FIG. 5,and those aspects may not be repeated here for the sake of brevity.

FIG. 4B shows a block diagram 400-b that illustrates a calibrationdevice 130-b for use in calibrating one or more sensors 111 of adistributed sensor system 100, in accordance with various aspects of thepresent disclosure. The calibration device 130-b is another example ofthe calibration devices 130 of FIGS.1 and 4A, and may implement variousaspects described with reference to FIGS. 2, 3A, 3B, 3C and 5. As shown,the calibration device 130-b includes a processor 410-a, a memory 420-a,at least one transceiver 440-a, at least one network port 445-a and atleast one antenna 450-a. Each of these components are in communication,directly or indirectly, with one another (e.g., over a bus 405-a). Eachof these components may perform the functions described above withreference to FIG. 4A.

In this example, the memory 420-a includes software that performs thefunctionality of a communications manager 430-a, a sensor identifier460-a, a sensor locator 470-a and a calibration model selector 480-a.The memory 420-a optionally may include a secondary parameter identifier490-a. For example, memory 420-a includes software that, when compiledand executed, causes the processor 410-a (or other components of thecalibration device 130-b) to perform the functionality described aboveand further below. A subset of the functionality of the communicationsmanager 430-a, the sensor identifier 460-a, the sensor locator 470-a,the calibration model selector 480-a can be included in memory 420-a;alternatively, all such functionality can be implemented as softwareexecuted by the processor 410-a to cause the calibration device 130-b toperform such functions. Other combinations of hardware/software can beused to perform the functions of the communications manager 430-a, thesensor identifier 460-a, the sensor locator 470-a, the calibration modelselector 480-a, and/or the secondary parameter identifier 490-a.

FIG. 5 is a flow chart illustrating an example of a method 500 fordistributed sensor calibration, in accordance with aspects of thepresent disclosure. The method 500 may be performed by any of thecalibration devices 130 as described with reference to FIGS. 1, 2, 3A,3B, 4A, or 4B. Broadly speaking, the method 500 illustrates a procedureby which a calibration device 130 performs distributed sensorcalibration.

At block 505 the calibration device 130 may identify first and secondsensors 111. The first and second sensors 111 may be configured tomeasure an environmental condition, such as a concentration or partialpressure of oxygen, carbon monoxide, carbon dioxide, sulfur dioxide,nitrogen oxides, water, and the like. Alternatively, the first andsecond sensors 111 may be configured to measure a broader condition suchas temperature, pressure, or density. Alternatively, the first andsecond sensors 111 may be configured to measure another environmentalconstituent, such as an atmospheric particulate concentration.

The first and second sensors 111 may be configured to measure anenvironmental condition directly, or indirectly. For example, a sensor111 may include a thermocouple that can be used to measure temperaturerelatively directly. In another example, a sensor assembly 110 may havean environmental sensor 111 that relies on multiple sensor elements tocalculate an environmental condition, in which case the sensor 111 maybe interpreted as measuring the environmental condition indirectly. Invarious examples, either or both of the first sensor 111 or the secondsensor 111 may measure an environmental condition directly orindirectly. Furthermore, the manner in which the first sensor 111 andthe second sensor 111 measure an environmental condition may be the sameor different.

Identifying a sensor 111 may include identifying a particular firstsensor 111 and second sensor 111 from a plurality of sensors 111identified in information received at the calibration device 130. Thisinformation may include sensor serial numbers or IDs, as well assupporting information such as the type of environmental data, the typeof the sensor 111, data format, data units, diagnostic information aboutthe sensor 111, and the like. After identifying the first and secondsensors, the method 500 may proceed to block 510.

At block 510 the calibration device 130 may collect sensed data of thefirst and second sensors 111. The sensed data from the first sensor 111and the sensed data from the second sensor 111 may correspond to thesame environmental condition. For instance, the sensed data from eachsensor 111 may be a concentration of a particular atmospheric gas, suchas carbon dioxide, in parts per million. In some examples, the senseddata from the first sensor 111 and the sensed data from the secondsensor 111 may correspond to the same environmental condition, but maybe in different formats. For instance, the calibration device 130 maycollect data of the first sensor 111 that represents a gas concentrationin parts per million, and data from the second sensor 111 thatrepresents a gas concentration in percentage. In some examples thecalibration device 130 may collect data of the first sensor 111 thatrepresents a gas concentration in parts per million and collect data ofthe second sensor 111 that represents a partial pressure. Thus, invarious examples the calibration device 130 may perform any ofcalculations, conversions, or approximations such that the sensed datafrom the first sensor 111 and the second sensor 111 can be suitablycompared or correlated. After collecting sensed data from the first andsecond sensors 111, the method 500 may proceed to block 510.

At block 515 the calibration device 130 may optionally determine adifference in quality between the sensed data of the first and secondsensors 111. As previously described, in various examples a differencein quality may be related to any of a grade or quality of components, anage of components, a level of noise in sensed data, an amount of senseddata, a periodicity or time period of sensed data, a data history, acalibration history, or a predetermined characteristic. Afterdetermining a difference in quality between the sensed data from thefirst and second sensors, the method 500 may proceed to block 520.

At block 520 the calibration device 130 may determine a directionalityof calibration using the determined difference in sensed data quality.For example, where sensors 111 can be compared by relative quality, thecalibration device may determine the directionality of calibration asusing data from a sensor 111 associated with sensed data having higherquality to calibrate a sensor 111 associated with sensed data having alower quality. In some examples, such as those relating to a periodicityof sensed data, a difference in quality may not refer to sensed datafrom two sensors 111 as being better or worse, but may instead refer toa particular characteristic of the sensed data. In such examples, thecharacteristic may be used to indicate a directionality of calibration.In other examples, sensed data of lower quality may still be used tocontribute to a calibration, but its effect may be given a reducedweight or significance to a calibration model. After determining adirectionality of calibration using the determined difference in senseddata quality, the method 500 may proceed to block 525.

At block 525 the calibration device 130 may identify whether a secondaryparameter may be affecting the sensed data. For example, the measurementa sensor 111 configured to measure certain environmental conditions mayrely on a secondary measurement, which may be provided to a sensor 111or a sensor assembly 110 including the sensor 111 by various means. Insuch examples, the calibration of the associated sensor 111 may rely onmeasurements of the secondary parameter associated with the first andsecond sensors 111 being within a certain range of each other. In someexamples, the calibration device 130 may include the secondary parameterin the determining a calibration model. If the calibration device 130identifies that a secondary parameter may be affecting the sensed data,the method 500 may proceed to block 530. If the calibration device 130does not identify a secondary parameter may be affecting the senseddata, the calibration device may proceed directly to block 535.

At block 530 the calibration device 130 may adjust sensed data from oneor both of the first sensor 111 or the second sensor 111 based on asecondary parameter. For instance, the calibration device 130 maycollect a secondary parameter measured by or otherwise provided by asensor assembly 110 including the sensor 111, or from any other source,and adjust the sensed data from that sensor 111 based on the secondaryparameter. In various examples the adjustment may be a conversion of thesensed data based on the secondary parameter, a filtering of the databased on the secondary parameter, or a selection of one or more portionsof the sensed data based on the secondary parameter. In some examples,the secondary parameter may be used to select a particular portion of acalibration model, such as a calibration within a particular temperatureband, a calibration within a particular pressure band, a calibrationwithin a particular time band, and so on. After adjusting the senseddata to account for the secondary parameter effects, the method 500 mayproceed to block 535.

At block 535 the calibration device 130 may determine a difference ingeospatial location between a location of the first sensor 111 and alocation of the second sensor 111. In some examples, determining thedifference in location between the sensors 111 may include determiningthe location of each of the first sensor 111 and the second sensor 111.In some examples the calibration device 130 may determine a location ofa sensor 111 by looking up a predetermined location stored in memoryassociated with the calibration device 130. In some examples thecalibration device may receive information associated with the locationof the sensor 111, which may be received from a database at a centralprocessing device 115 or received from a sensor assembly 110 thatincludes the sensor 111. In examples where a sensor assembly 110transmits location information, the location information may bepredetermined information stored at the sensor assembly 110, or may beotherwise measured by the sensor assembly 110. For instance, in someexamples the sensor assembly 110 may be configured to determine alocation or a sensor 111 by receiving signals from a GPS or GLONASSsatellite constellation, or any other terrestrial or satellite-basedsystem that provides signals that can be used for positioning. Invarious examples, the location of the sensor 111 as determined by asensor assembly 110 can be transmitted to the calibration device 130 bya wired or wireless communication link 135.

In some examples, the difference in geospatial location between thefirst sensor 111 and the second sensor 111 may be a one-dimensionaldistance value. In some examples the difference in geospatial locationmay be a two- or three- dimensional value, such as a vector. Forinstance, the difference in geospatial location may reflect atwo-dimensional projection of distance at a reference elevation oraltitude, such as at sea level or any other elevation or altitude. Atwo-dimensional projection may be characterized by a difference inlatitude and longitude, a difference in distance in a north-southdirection and a distance in an east-west direction, a vector having adistance magnitude and a cardinal direction, or any other suitabletwo-dimensional representation. A three-dimensional difference ingeospatial location may be characterized by a difference in latitude,longitude, and elevation, a vector having a distance magnitude andangles in two principal directions, or any other suitablethree-dimensional representation. After determining a difference ingeospatial location between a location of the first sensor and alocation of the second sensor, the method 500 may proceed to block 540.

At block 540 the calibration device 130 may identify whether the firstsensor 111 and the second sensor 111 are separated by a variablerelative position. For instance, one or both of the first sensor 111 orthe second sensor 111 may be moving, in which case the relative positionbetween them may change. In some examples, the calibration device 130may simply identify that the distance between the first sensor 111 andthe second sensor 111 changes over time. In some examples, thecalibration device may determine that both a distance and an orientationfrom the first sensor 111 to the second sensor 111 has changed. Ineither case, if the calibration device has identified that the firstsensor 111 and the second sensor 111 are separated by a variablerelative position, the method 500 may proceed from block 540 to block555. If no change in relative position is identified, the method mayproceed to block 545.At block 545, the method 500 can includedetermining a calibration model using the determined difference ingeospatial location between the first and second sensors 111. Forinstance, as previously described the calibration device 130 may applylow-pass filtering to sensed data from one of both of the first andsecond sensors 111 based on the distance between sensors 111, and thenuse a correlation between the filtered data from the first and secondsensors 111 to determine a calibration model. In other examples, thecalibration device may modify portions of the sensed data based on arelative orientation between the first sensor 111 and the second sensor111. For example, the calibration device 130 may have data or otherunderstanding of a prevailing wind or some other source that may beaffecting an environmental condition, and may impose a time offset orother phase lag to sensed data corresponding to an environmentalmeasurement from one or both of the first sensor 111 or second sensor111, and use a correlation of the adjusted data to determine acalibration model.

In some examples, determining a calibration model may include rejectingat least a portion of the data entirely from a calibration. For example,data collected from a sensor 111 may be rejected from a calibrationmodel if the sensor 111 is too far from a target sensor 111 to becalibrated. In some examples, the calibration device 130 may reject databased on identifying or suspecting an error condition associated withthe sensor 111. Such identified or suspected error conditions mayinclude an identified drop-out, a sensor “railed” to an output limit, anoutput that is erroneously locked at an output value, an output that isdrifting away from a plausible value, an erratic or noisy output, andthe like.

Determining a calibration model may include various means of correlationbetween data associated with the first and second sensors 111, andadjustment of parameters associated with one or both of the first orsecond sensors 111. For instance, the calibration device 130 mayidentify that data for an environmental condition associated with thefirst sensor 111 is lower than data for the environmental conditionassociated with the second sensor 111. The calibration device 130 mayhave various information or criteria to suggest that the first andsecond sensors 111 should be more closely correlated than theirtransmitted data may suggest. Therefore, the calibration device 130 maydetermine a calibration model that includes increasing a sensor gain orsensor offset associated with the first sensor. After determining thecalibration model using the determined difference, the method 500 canproceed to block 555.

At block 550, in examples where the calibration device 130 hasdetermined a variable relative position between the first and secondsensors, the method 500 can include selecting or adjusting portions ofthe sensed data and determining a calibration model using the determineddifference in geospatial location between the first and second sensors111.

For example, when applying a frequency-based decomposition or filteringas part of the calibration model, a low-pass filter cutoff frequency maybe higher for portions of the data where the sensors 111 are moreclosely located, and a low-pass filter cutoff frequency may be lower forportions of the data where the sensors 111 are located farther apart. Ifthe distance between the first sensor 111 and the second sensor 111 isgreater than a threshold value, portions of the sensed data may beignored altogether.

In examples where the relative orientation between the first and secondsensor 111 changes with respect to the direction of prevailing wind, thecalibration device 130 may apply a different time offset or phase lag toeach sensor during various portions of the sensed data. In this way,measurements of transient phenomena can be more accurately aligned,providing a more accurate set of data for calibration.

Following the selection or adjustment of portions of the sensed data,the calibration device 130 can apply any of the techniques previouslydescribed to determine a calibration model. For instance, thecalibration device 130 may determine adjustments to be applied to thecalibration of the first sensor 111, such as an adjustment to a sensorgain, a sensor offset, or any other parameter used to convert a physicalmeasurement taken by the sensor 111 to data representing theenvironmental condition. After selecting portions of the sensed data anddetermining the calibration model using the determined difference, themethod 500 can proceed to block 555.

At block 555, the calibration device 130 may apply the determinedcalibration model for sensor calibration. As previously described, thecalibration model may be applied directly at a sensor 111, at a portionof a sensor assembly 110 that contains the sensor 111, or at any otherdevice that receives sensed data from the sensor, such as a centralprocessing device 115 or other data acquisition unit. Thus, thecalibration device 130 may send an instruction to adjust a computationalparameter that converts a physical sensor measurement to environmentalcondition data, with the instruction sent to any one or more of thesedevices.

The calibration device 130 may additionally provide any one or more ofthese devices with at least a portion of the calibration model asnecessary to improve the correlation of sensors 111 in the distributedsensor system 100. For instance, upon receiving at least a portion of acalibration model, a sensor 111, a sensor assembly 110, or a centralprocessing device 115 can make an adjustment to a sensor gain, sensoroffset, or any other parameter that converts a physical measurement intouseful data representing the measured environmental condition. In someexamples, calibration information for a plurality of sensors 111 may becommunicated by the calibration device 130 to a central processingdevice 115, and the central processing device 115 can use calibrationinformation for the plurality of sensors 111 to adjust sensed data fromthe plurality of sensors 111 to improve correlation in the distributedsensor system 100.

Thus, the method 500 provides for calibrating a sensor 111 in adistributed sensor system 100. It should be noted that the method 500 isjust one implementation and that the operations of the method 500 can berearranged or otherwise modified such that other implementations arepossible. For example, blocks 515 and 520, blocks 525 and 530, andblocks 540 and 550 as depicted in dashed lines may be optional. Forexample, blocks 515 and 520 may be omitted if the calibration isperformed on both the first sensor and the second sensor. Further, ifboth the first sensor and the second sensor are stationary (e.g., fixedin location), then blocks 540 and 550 may be omitted.

Furthermore, although the method 500 is described with reference to afirst sensor 111 and a second sensor 111, it should be appreciated thata distributed sensor calibration may be performed between any number ofsensors. For example, a calibration device may identify a plurality ofsensors 111, collect data of the plurality of sensors 111, and determinea calibration model based on the plurality of sensors 111. Thecalibration device may apply weighting to the sensed data collected fromindividual sensors 111 based on such factors as a location of thesensors 111 or a difference in location between various individualsensors 111 and a sensor 111 to be calibrated.

The detailed description set forth above in connection with the appendeddrawings describes examples and does not represent the only examplesthat may be implemented or that are within the scope of the claims. Theterms “example” and “exemplary,” when used in this description, mean“serving as an example, instance, or illustration,” and not “preferred”or “advantageous over other examples.” The detailed description includesspecific details for the purpose of providing an understanding of thedescribed techniques. These techniques, however, may be practicedwithout these specific details. In some instances, well-known structuresand apparatuses are shown in block diagram form in order to avoidobscuring the concepts of the described examples.

Information and signals may be represented using any of a variety ofdifferent technologies and techniques. For example, data, instructions,commands, information, signals, bits, symbols, and chips that may bereferenced throughout the above description may be represented byvoltages, currents, electromagnetic waves, magnetic fields or particles,optical fields or particles, or any combination thereof.

The various illustrative blocks and components described in connectionwith the disclosure herein may be implemented or performed with ageneral-purpose processor, a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a field programmablegate array (FPGA) or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Ageneral-purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, multiple microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration.

The functions described herein may be implemented in hardware, softwareexecuted by a processor, firmware, or any combination thereof. Ifimplemented in software executed by a processor, the functions may bestored on or transmitted over as one or more instructions or code on acomputer-readable medium. Other examples and implementations are withinthe scope and spirit of the disclosure and appended claims. For example,due to the nature of software, functions described above can beimplemented using software executed by a processor, hardware, firmware,hardwiring, or combinations of any of these. Features implementingfunctions may also be physically located at various positions, includingbeing distributed such that portions of functions are implemented atdifferent physical locations. As used herein, including in the claims,the term “and/or,” when used in a list of two or more items, means thatany one of the listed items can be employed by itself, or anycombination of two or more of the listed items can be employed. Forexample, if a composition is described as containing components A, B,and/or C, the composition can contain A alone; B alone; C alone; A and Bin combination; A and C in combination; B and C in combination; or A, B,and C in combination. Also, as used herein, including in the claims,“or” as used in a list of items (for example, a list of items prefacedby a phrase such as “at least one of” or “one or more of”) indicates adisjunctive list such that, for example, a list of “at least one of A,B, or C” means A or B or C or AB or AC or BC or ABC (i.e., A and B andC).

Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage medium may be anyavailable medium that can be accessed by a general purpose or specialpurpose computer. By way of example, and not limitation,computer-readable media can comprise RAM, ROM, EEPROM, flash memory,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code means in the form of instructions or datastructures and that can be accessed by a general-purpose orspecial-purpose computer, or a general-purpose or special-purposeprocessor. Also, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, include compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk and Blu-ray discwhere disks usually reproduce data magnetically, while discs reproducedata optically with lasers. Combinations of the above are also includedwithin the scope of computer-readable media.

The previous description of the disclosure is provided to enable aperson skilled in the art to make or use the disclosure. Variousmodifications to the disclosure will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other variations without departing from the scope of thedisclosure. Thus, the disclosure is not to be limited to the examplesand designs described herein but is to be accorded the broadest scopeconsistent with the principles and novel features disclosed herein.

What is claimed is:
 1. A method for calibrating an environmental sensorof a distributed environmental sensor system, comprising: collectingsensed data of a first environmental sensor and a second environmentalsensor of the distributed environmental sensor system; determining adifference in geospatial location between a location of the firstenvironmental sensor and a location of the second environmental sensor;determining a calibration model based at least in part on the determineddifference in geospatial location; and calibrating the firstenvironmental sensor using the determined calibration model and thesensed data of the second environmental sensor.
 2. The method of claim1, wherein calibrating the first environmental sensor comprises: sendingan instruction to adjust a computational parameter that converts aphysical sensor measurement to environmental condition data, wherein theinstruction is sent to any one or more of: the first environmentalsensor, an assembly that includes the first environmental sensor, or adevice that receives data from the first environmental sensor.
 3. Themethod of claim 1, wherein calibrating the first environmental sensorcomprises: correlating the sensed data of the first environmental sensorwith the sensed data of the second environmental sensor.
 4. The methodof claim 3, wherein calibrating the first environmental sensorcomprises: performing a frequency-based decomposition of the sensed dataof the first and second environmental sensors.
 5. The method of claim 4,wherein determining the calibration model comprises: selecting a portionof the decomposed data of the first environmental sensor and a portionof the decomposed data of the second environmental sensor, wherein theselection is based at least in part on the determined difference ingeospatial location.
 6. The method of claim 5, wherein calibrating thefirst environmental sensor comprises: correlating the selected portionsof the decomposed data of the first and second environmental sensors. 7.The method of claim 1, further comprising: determining a difference in aquality between the sensed data of the first environmental sensor and aquality of the sensed data of the second environmental sensor; anddetermining a directionality of the calibration based at least in parton the determined difference in the quality.
 8. The method of claim 7,wherein the calibration of the first environmental sensor occurs whenthe quality of the sensed data of the first environmental sensor islower than the quality of the sensed data of the second environmentalsensor.
 9. The method of claim 7, wherein the quality of the sensed dataof at least one of the first environmental sensor and the secondenvironmental sensor is based at least in part on an amount of thesensed data provided by that environmental sensor, a data history ofthat environmental sensor, a periodicity of data provided by thatenvironmental sensor assembly, a calibration history of thatenvironmental sensor, or a predetermined characteristic of thatenvironmental sensor.
 10. The method of claim 1, wherein calibrating thefirst environmental sensor comprises: selecting a portion of the senseddata of the first environmental sensor and a corresponding portion ofthe sensed data of the second environmental sensor based at least inpart on the determined difference in geospatial location over time. 11.The method of claim 1, further comprising: determining a parameter thataffects the sensed data of the first environmental sensor; and adjustingthe sensed data of the first environmental sensor based on an effect ofthe parameter, the adjustment being performed prior to performing thecalibration.
 12. The method of claim 1, further comprising: calibratingthe second environmental sensor using the determined calibration modeland the sensed data of the first environmental sensor.
 13. A device forcalibrating an environmental sensor of a distributed environmentalsensor system, comprising: a processor and memory communicativelycoupled to the processor, the memory comprising computer-readable codethat, when executed by the processor, causes the device to: collectsensed data of a first environmental sensor and a second environmentalsensor of the distributed environmental sensor system; determine adifference in geospatial location between a location of the firstenvironmental sensor and a location of the second environmental sensor;determine a calibration model based at least in part on the determineddifference in geospatial location; and calibrate the first environmentalsensor using the determined calibration model and the sensed data of thesecond environmental sensor.
 14. The device of claim 13, wherein thecomputer-readable code causes the device to calibrate the firstenvironmental sensor by: sending an instruction to adjust acomputational parameter that converts a physical sensor measurement toenvironmental condition data, wherein the instruction is sent to any oneof: the first environmental sensor, an assembly that includes the firstenvironmental sensor, or a device that receives data from the firstenvironmental sensor.
 15. The device of claim 13, wherein thecomputer-readable code causes the device to calibrate the firstenvironmental sensor by: correlating the sensed data of the firstenvironmental sensor with the sensed data of the second environmentalsensor.
 16. The device of claim 15, wherein the computer-readable codecauses the device to calibrate the first environmental sensor by:performing a frequency-based decomposition of the sensed data of thefirst and second environmental sensors.
 17. The device of claim 13,wherein the computer-readable code further causes the device to:determining a difference in a quality between the sensed data of thefirst environmental sensor and a quality of the sensed data of thesecond environmental sensor; and determining a directionality of thecalibration based at least in part on the determined difference in thequality.
 18. The device of claim 17, wherein the computer-readable codecauses the device to calibrate the first environmental sensor when thequality of the sensed data of the first environmental sensor is lowerthan the quality of the sensed data of the second environmental sensor.19. The device of claim 17, wherein the quality of the sensed data of atleast one of the first environmental sensor and the second environmentalsensor is based at least in part on an amount of the sensed dataprovided by that environmental sensor, a data history of thatenvironmental sensor, a periodicity of data provided by thatenvironmental sensor assembly, a calibration history of thatenvironmental sensor, or a predetermined characteristic of thatenvironmental sensor.
 20. A non-transitory computer-readable mediumcomprising computer-readable code that, when executed, causes a deviceto: collect sensed data of a first environmental sensor and a secondenvironmental sensor of a distributed environmental sensor system;determine a difference in geospatial location between a location of thefirst environmental sensor and a location of the second environmentalsensor; determine a calibration model based at least in part on thedetermined difference in geospatial location; and calibrate the firstenvironmental sensor using the determined calibration model and thesensed data of the second environmental sensor.